The intensive computation of Discrete Wavelet Transform (DWT) due to its inherent multilevel data decomposition and reconstruction operations brings a bottleneck that drastically reduces its performance and implementations for real-time applications when facing large size digital images and/or high-definition videos. Although various software-based acceleration solutions, such as the lifting scheme, have been devised and achieved a higher performance in general, the pure software accelerated DWT still struggle to cope with the demands from real-time and interactive applications. With the growing capacity and popularity of graphics hardware, personal computers (PCs) nowadays are often equipped with programmable Graphics Processing Units (GPUs) for graphics acceleration. The GPU offers a cost-effective parallel data processing mechanism for operations on large amount of data, even for applications beyond graphics. This practice is commonly referred as General-purpose Computing on GPU (GPGPU). This paper presented a GPGPU framework with the corresponding parallel computing solution for waveletbased image denoising by using off-the-shelf consumer-grade programmable GPUs. This framework can be readily incorporated with different forms of DWT by customising the parameter of the wavelet kernel. Experiment results show that the framework gains applicability in data parallelism and satisfaction performance in accelerating computations for wavelet-based denoising . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 However, the intensive computation of DWT due to its inherent multilevel data decomposition and reconstruction operations brings a bottleneck that drastically reduces its performance and implementations for real-time applications when facing large size digital images and/or high-definition videos. Although various software-based acceleration solutions, such as the lifting scheme, have been devised and achieved a higher performance in general, the pure software accelerated DWT still struggle to cope with the demands from real-time and interactive applications. With the growing capacity and popularity of graphics hardware, personal computers (PCs) nowadays are often equipped with programmable Graphics Processing Units (GPUs) for graphics acceleration. The GPU offers a cost-effective parallel data processing mechanism for operations on large amount of data, even for applications beyond graphics. This practice is commonly referred as General-purpose Computing on GPU (GPGPU). This paper presented a GPGPU framework with the corresponding parallel computing solution for wavelet-based image denoising by using off-the-shelf consumer-grade programmable GPUs. This framework can be readily incorporated with different forms of DWT by customising the parameter of the wavelet kernel. Experiment results show that the framework...

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Background: Clinically, irregular protrusions and blockages after stent deployment can lead to significant adverse outcomes such as thrombotic reocclusion or restenosis. In this study, we propose a novel fully automated method for irregular lumen segmentation and normal/abnormal lumen classification. Methods:The proposed method consists of a lumen segmentation, feature extraction, and lumen classification. In total, 92 features were extracted to classify normal/abnormal lumen. The lumen classification method is a combination of supervised learning algorithm and feature selection that is a partition-membership filter method.Results: As the results, our proposed lumen segmentation method obtained the average of dice similarity coefficient (DSC) and the accuracy of proposed features and the random forest (RF) for normal/abnormal lumen classification as 97.6% and 98.2%, respectively.Conclusions: Therefore, we can lead to better understanding of the overall vascular status and help to determine cardiovascular diagnosis. K E Y W O R D S cardiac, cardiology, heart, image analysis, vascular surgery, vessel

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This paper presents a relaxation labeling

The discrete wavelet transform (DWT) has been widely used in various scientific and engineering fields. However, the enormous computation of DWT caused by multilevel filtering/down-sampling is a bottleneck that limits the application of DWT used in real-time environment where the data size is large. A stream-based parallel computation framework to accelerate the implementation of DWT is presented in this paper, which is based on employing the consumer-level programmable graphics hardware. Simulation results show that, this stream-based parallel computation framework can achieve a significant performance gain on algorithm acceleration comparing with those completely CPUbased solutions for DWT.

The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and waveletbased image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.